首页|严重遮挡场景下AOA-ENN辅助列车定位的方法研究

严重遮挡场景下AOA-ENN辅助列车定位的方法研究

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
铁路周边卫星遮挡情况复杂多变,当列车在隧道等严重遮挡场景下运行时,北斗卫星导航系统/捷联惯性导航系统(BDS/SINS)列车组合定位系统无法接收到卫星信号,导致列车定位误差累积甚至定位失效.为提高列车在严重遮挡场景下的定位精度,提出阿基米德优化算法优化的Elman神经网络(AOA-ENN)辅助BDS/SINS列车组合定位系统进行列车定位的方法.首先,在无迹卡尔曼滤波算法中引入新息理论得到自适应无迹卡尔曼滤波算法(AUKF),将其作为BDS/SINS列车组合定位系统的信息融合算法.其次,基于模糊C均值聚类算法(FCM)建立列车运行场景识别模型,依据环境特征参数对列车运行场景进行自主识别.最后根据场景识别模型的输出结果,当列车在开阔、低遮挡、高遮挡场景运行时,通过AUKF对BDS和SINS解算的定位信息进行融合来完成列车定位,同时将采集的列车定位数据加入训练集,对AOA-ENN进行在线训练;当列车在严重遮挡场景下运行时,BDS无法正常接收信号,利用训练好的AOA-ENN辅助列车组合定位系统进行定位,利用AUKF对AOA-ENN的预测信息和SINS解算的信息进行融合后输出定位结果.实验结果表明:在严重遮挡场景下,AOA-ENN辅助列车组合定位系统得到的定位成功率达到98.2%;通过不同优化算法和神经网络的仿真对比实验,验证了AOA-ENN在辅助列车组合定位系统定位时的优越性.所得成果为优化列车在隧道等严重遮挡场景下的定位精度提供了参考.
AOA-ENN assisted train positioning in severe occlusion scenarios
The satellite occlusion situation around the railway is complicated and variable.When trains operate in severe occlusion scenarios such as tunnels,the Beidou satellite navigation system/strapdown inertial navigation system(BDS/SINS)integrated train positioning system cannot receive satellite signals,resulting in the accumulation of train positioning errors and even positioning failure.To improve the positioning accuracy of the train in severe occlusion scenarios,an Elman neural network optimized by Archimedes optimization algorithm(AOA-ENN)was proposed to assist the BDS/SINS integrated train positioning system.Firstly,the innovation theory was introduced into the unscented Kalman filter algorithm to obtain the adaptive unscented Kalman filter algorithm(AUKF),which was used as the information fusion algorithm of the BDS/SINS integrated train positioning system.Secondly,the train operation scenarios recognition model was established based on the fuzzy C-means clustering algorithm(FCM),which could recognize train operation scenarios autonomously according to the environmental characteristic parameters.Finally,the scenarios recognition model outputted judgment results.The train run in the open scenarios,low-occlusion scenarios,and high-occlusion scenarios,the positioning information of BDS.The SINS was fused by AUKF to complete the train positioning.At the same time,the collected train positioning data was added to the training set for online training of AOA-ENN.When the train was running in severe occlusion scenarios,BDS could not receive the signal normally.The trained AOA-ENN was used to assist the integrated train positioning system.The AUKF was used to fuse the prediction information of AOA-ENN and the information calculated by SINS to gain the positioning results.The experimental results indicate that the positioning success rate of the AOA-ENN assisted train integrated positioning system reaches 98.2%in severe occlusion scenarios.Simultaneously,through the comparison experiments of different optimization algorithms and neural networks,the superiority of AOA-ENN in assisting the positioning of the integrated train positioning system is verified.The results can provide a reference for optimizing the positioning accuracy of trains in severe occlusion scenarios such as tunnels.

integrated train positioning systemoperation scenarios recognitionadaptive unscented Kalman filterArchimedes optimization algorithmElman neural network

武晓春、杨伟康

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兰州交通大学 自动化与电气工程学院,甘肃 兰州 730070

列车组合定位系统 运行环境识别 自适应无迹卡尔曼滤波 阿基米德优化算法 Elman神经网络

中国国家铁路集团有限公司基金资助项目国家自然科学基金资助项目

N2022G01261661027

2024

10.19713/j.cnki.43-1423/u.T20231656

铁道科学与工程学报
中南大学 中国铁道学会

铁道科学与工程学报

CSTPCD北大核心EI
影响因子：0.837
ISSN：1672-7029
年,卷(期)：2024.21(7)
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